2 citations · 9 across the 15 of their papers we have counts for
6 papers · 1 filter
CTRL-D: Controllable Dynamic 3D Scene Editing with Personalized 2D Diffusion
Kai He, Chin-Hsuan Wu, Igor Gilitschenski
Recent advances in 3D representations, such as Neural Radiance Fields and 3D Gaussian Splatting, have greatly improved realistic scene modeling and novel-view synthesis. However, a…
EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering
Toshiya Yura, Ashkan Mirzaei, Igor Gilitschenski
We introduce a method for using event camera data in novel view synthesis via Gaussian Splatting. Event cameras offer exceptional temporal resolution and a high dynamic range. Leve…
Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Hanxue Liang, Jiawei Ren, Ashkan Mirzaei +8
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle wi…
Towards Unsupervised Blind Face Restoration using Diffusion Prior
Tianshu Kuai, Sina Honari, Igor Gilitschenski +1
Blind face restoration methods have shown remarkable performance, particularly when trained on large-scale synthetic datasets with supervised learning. These datasets are often gen…
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting
Umangi Jain, Ashkan Mirzaei, Igor Gilitschenski
We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented b…
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
Ashkan Mirzaei, Riccardo De Lutio, Seung Wook Kim +5
Neural reconstruction approaches are rapidly emerging as the preferred representation for 3D scenes, but their limited editability is still posing a challenge. In this work, we pro…